Chest X-ray in suspected lung cancer is harmful

Foley, R.; Nassour, Vanessa; Oliver, Helen; Hall, Toby; Masani, Vidan; Robinson, Graham; Rodrigues, Jonathan; Hudson, Benjamin

doi:10.1007/s00330-021-07708-0

Cited by 16 publications

(10 citation statements)

References 10 publications

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“…The potential usefulness of cut-offs between the percentiles 98 and 66 would need further research. The use of the proposed CAD algorithm on chest x-rays unread by a radiologist would favour the identification of patients who require fast-track access to chest computed tomography 42 , considering that this technique is the current gold standard for thoracic imaging in suspected LC. The performed research has focused in chest x-rays unread by a radiologist, and the obtained results cannot be extrapolated to chest images that include a radiology report.…”

Section: Discussionmentioning

confidence: 99%

Computer-assisted diagnosis for an early identification of lung cancer in chest X rays

Juan,

Monsó,

Lozano

et al. 2023

Sci Rep

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Computer-assisted diagnosis (CAD) algorithms have shown its usefulness for the identification of pulmonary nodules in chest x-rays, but its capability to diagnose lung cancer (LC) is unknown. A CAD algorithm for the identification of pulmonary nodules was created and used on a retrospective cohort of patients with x-rays performed in 2008 and not examined by a radiologist when obtained. X-rays were sorted according to the probability of pulmonary nodule, read by a radiologist and the evolution for the following three years was assessed. The CAD algorithm sorted 20,303 x-rays and defined four subgroups with 250 images each (percentiles ≥ 98, 66, 33 and 0). Fifty-eight pulmonary nodules were identified in the ≥ 98 percentile (23,2%), while only 64 were found in lower percentiles (8,5%) (p < 0.001). A pulmonary nodule was confirmed by the radiologist in 39 out of 173 patients in the high-probability group who had follow-up information (22.5%), and in 5 of them a LC was diagnosed with a delay of 11 months (12.8%). In one quarter of the chest x-rays considered as high-probability for pulmonary nodule by a CAD algorithm, the finding is confirmed and corresponds to an undiagnosed LC in one tenth of the cases.

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Section: Discussionmentioning

confidence: 99%

Computer-assisted diagnosis for an early identification of lung cancer in chest X rays

Juan,

Monsó,

Lozano

et al. 2023

Sci Rep

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“…When clinical suspicion for lung cancer is high, a negative chest radiograph provides only limited reassurance 26. When the radiograph is requested, explain that further assessment may be required as lung cancer is not always identified with radiography.…”

Section: What Is the Next Investigation?mentioning

confidence: 99%

Investigating suspected lung cancer

Bradley¹,

Bhartia²,

Kennedy³

et al. 2022

BMJ

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“…1,2 Early diagnosis and treatment are considered critical to lowering lung cancer morbidity and mortality, and the 5 year survival rate for I-stage lung cancer patients can reach 90%. 3,4 However, early-stage cancer tissue is difficult to detect, and conventional clinical diagnosis methods, including bronchoscopy after chest X-ray, 5,6 cytology after sputum analysis, 7−10 and low-dose computed tomography 11,12 cannot meet accurate detection requirements. Recently, many clinical cases have confirmed that some proteins [e.g., carcinoembryonic antigen (CEA)] or cytokines 13−17 in the serum of lung cancer patients, even at the early stage, are called serum tumor biomarkers, the abundance of which is related to the development status of lung cancer, and its types relate to different histological subtypes.…”

Section: ■ Introductionmentioning

confidence: 99%

“…Lung cancer remains the leading cause of cancer-related death. There were 19.3 million new cancer cases and 10 million deaths worldwide by 2020, among which an estimated 1.8 million deaths (18%) were due to lung cancer. , Early diagnosis and treatment are considered critical to lowering lung cancer morbidity and mortality, and the 5 year survival rate for I-stage lung cancer patients can reach 90%. , However, early-stage cancer tissue is difficult to detect, and conventional clinical diagnosis methods, including bronchoscopy after chest X-ray, , cytology after sputum analysis, − and low-dose computed tomography , cannot meet accurate detection requirements. Recently, many clinical cases have confirmed that some proteins [e.g., carcinoembryonic antigen (CEA)] or cytokines − in the serum of lung cancer patients, even at the early stage, are called serum tumor biomarkers, the abundance of which is related to the development status of lung cancer, and its types relate to different histological subtypes. , In this regard, accurate quantification of serum tumor biomarkers will provide a new method for the painless and non-invasive diagnosis of lung cancer.…”

Section: Introductionmentioning

confidence: 99%

Carbon Nanotube Field-Effect Transistor Biosensor with an Enlarged Gate Area for Ultra-Sensitive Detection of a Lung Cancer Biomarker

Liu

Wei

et al. 2023

ACS Appl. Mater. Interfaces

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Carcinoembryonic antigen (CEA) is a recognized biomarker for lung cancer and can be used for early detection. However, the clinical value of CEA is not fully realized due to the rigorous requirement for high-sensitivity and wide-range detection methods. Field-effect transistor (FET) biosensors, as one of the potentially powerful platforms, may detect CEA with a significantly higher sensitivity than conventional clinical testing equipment, while their sensitivity and detection range for CEA are far below the requirement for early detection. Here, we construct a floating gate FET biosensor to detect CEA based on a semiconducting carbon nanotube (CNT) film combined with an undulating yttrium oxide (Y2O3) dielectric layer as the biosensing interface. Utilizing an undulating biosensing interface, the proposed device showed a wider detection range and optimized sensitivity and detection limit, which benefited from an increase of probe-binding sites on the sensing interface and an increase of electric double-layer capacitance, respectively. The outcomes of analytical studies confirm that the undulating Y2O3 provided the desired biosensing surface for probe immobilization and performance optimization of a CNT-FET biosensor toward CEA including a wide detection range from 1 fg/mL to 1 ng/mL, good linearity, and high sensitivity of 72 ag/mL. More crucially, the sensing platform can function normally in the complicated environment of fetal bovine serum, indicating its great promise for early lung cancer screening.

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Chest X-ray in suspected lung cancer is harmful

Cited by 16 publications

References 10 publications

Computer-assisted diagnosis for an early identification of lung cancer in chest X rays

Computer-assisted diagnosis for an early identification of lung cancer in chest X rays

Investigating suspected lung cancer

Carbon Nanotube Field-Effect Transistor Biosensor with an Enlarged Gate Area for Ultra-Sensitive Detection of a Lung Cancer Biomarker

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